{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "import collections\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import random\n",
    "import zipfile\n",
    "import numpy as np\n",
    "from tempfile import gettempdir\n",
    "import urllib\n",
    "import pprint\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Give a folder path as an argument with '--log_dir' to save\n",
    "# TensorBoard summaries. Default is a log folder in current directory.\n",
    "#current_path = os.path.dirname(os.path.realpath(sys.argv[0]))\n",
    "current_path = '/Users/limeng/Desktop/ML_DL_learn/CSDN/深度学习神经网络/W10/'\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument(\n",
    "    '--log_dir',\n",
    "    type=str,\n",
    "    default=os.path.join(current_path, 'log'),\n",
    "    help='The log directory for TensorBoard summaries.')\n",
    "FLAGS, unparsed = parser.parse_known_args()\n",
    "\n",
    "# Create the directory for TensorBoard variables if there is not.\n",
    "if not os.path.exists(FLAGS.log_dir):\n",
    "  os.makedirs(FLAGS.log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'潘阆\\n\\n酒泉子（十之一）\\n\\n长忆钱塘，不是人寰是天上。万家掩映翠微间。处处水潺潺。\\n\\n异花四季当窗放。出入分明在屏障。别来隋柳几经秋。何日得重游。\\n\\n酒泉子（十之二）\\n\\n长忆钱塘，临水傍山三百寺。僧房携杖遍曾游。闲话觉忘忧。\\n\\n栴檀楼阁云霞畔。钟梵清宵彻天汉。别来遥礼只焚香。便恐是西方。\\n\\n酒泉子（十之三）\\n\\n长忆西湖，湖上春来无限景。吴姬个个是神仙。竞泛木兰船。\\n\\n楼台簇簇疑蓬岛。野人只合其中老'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read_data\n",
    "filename = \"quiz-w10-code/QuanSongCi.txt\"\n",
    "def read_data(filename):\n",
    "    with open(filename, \"r\", encoding=\"UTF-8\") as f:\n",
    "        data = f.read()\n",
    "    return data\n",
    "words = read_data(filename)\n",
    "words[0:200]\n",
    "#print('Data size', len(words))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndef cut_comma(text):\\n    tex = [i.strip('\\n').strip('\\r').strip('。').strip('，|）|□|（|、|<|:|\\n') for i in text]\\n    return list(filter(None,tex))\\nwords = cut_comma(words)\\nprint('Data size', len(words))\\nwords[0:200]\\n\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去标点\n",
    "'''\n",
    "def cut_comma(text):\n",
    "    tex = [i.strip('\\n').strip('\\r').strip('。').strip('，|）|□|（|、|<|:|\\n') for i in text]\n",
    "    return list(filter(None,tex))\n",
    "words = cut_comma(words)\n",
    "print('Data size', len(words))\n",
    "words[0:200]\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "出现频率最高的单词（包括未知类别的）： [['unkown', 1196], ('。', 149620), ('\\n', 117070), ('，', 108451), ('、', 19612), ('人', 13607), ('花', 12809), ('风', 12568), ('一', 11301), ('（', 10967)]\n",
      "样本数据(排名)： [1503, 1828, 2, 2, 40, 613, 47, 9, 111, 117] \n",
      "对应的单词 ['潘', '阆', '\\n', '\\n', '酒', '泉', '子', '（', '十', '之']\n"
     ]
    }
   ],
   "source": [
    "#4.构建词汇表，并统计每个单词出现的频数，同时用字典的形式进行存储，取频数排名前50000的单词\n",
    "vocabulary_size = 5000\n",
    "def build_dataset(words):\n",
    "    count = [[\"unkown\",-1]]\n",
    "    #collections.Counter()返回的是形如[[\"unkown\",-1],(\"the\",4),(\"physics\",2)]\n",
    "    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "    dictionary = {}\n",
    "    #将全部单词转为编号（以频数排序的编号），我们只关注top50000的单词，以外的认为是unknown的，编号为0，同时统计一下这类词汇的数量\n",
    "    for word,_ in count:\n",
    "        dictionary[word] = len(dictionary)\n",
    "        #形如：{\"the\"：1，\"UNK\"：0，\"a\"：12}\n",
    "    data = []\n",
    "    unk_count = 0 #准备统计top50000以外的单词的个数\n",
    "    for word in words:\n",
    "        #对于其中每一个单词，首先判断是否出现在字典当中\n",
    "        if word in dictionary:\n",
    "            #如果已经出现在字典中，则转为其编号\n",
    "            index = dictionary[word]\n",
    "        else:\n",
    "            #如果不在字典，则转为编号0\n",
    "            index = 0\n",
    "            unk_count += 1\n",
    "        data.append(index)#此时单词已经转变成对应的编号\n",
    "    '''\n",
    "    print(data[:10]) \n",
    "    #[5234, 3081, 12, 6, 195, 2, 3134, 46, 59, 156]\n",
    "    '''\n",
    "    count[0][1] = unk_count #将统计好的unknown的单词数，填入count中\n",
    "    #将字典进行翻转,形如：{3:\"the,4:\"an\"}\n",
    "    reverse_dictionary = dict(zip(dictionary.values(),dictionary.keys()))\n",
    "    return data,count,dictionary,reverse_dictionary\n",
    "#为了节省内存，将原始单词列表进行删除\n",
    "data,count,dictionary,reverse_dictionary = build_dataset(words)\n",
    "del words\n",
    "#将部分结果展示出来\n",
    "print(\"出现频率最高的单词（包括未知类别的）：\",count[:10])\n",
    "#将已经转换为编号的数据进行输出，从data中输出频数，从翻转字典中输出编号对应的单词\n",
    "print(\"样本数据(排名)：\",data[:10],\"\\n对应的单词\",[reverse_dictionary[i] for i in data[:10]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "目标单词：阆       对应编号为：       1828   对应的语境单词为:        潘    编号为 1503\n",
      "目标单词：阆       对应编号为：       1828   对应的语境单词为:        \n",
      "    编号为 2\n",
      "目标单词：\n",
      "       对应编号为：       2   对应的语境单词为:        阆    编号为 1828\n",
      "目标单词：\n",
      "       对应编号为：       2   对应的语境单词为:        \n",
      "    编号为 2\n",
      "目标单词：\n",
      "       对应编号为：       2   对应的语境单词为:        \n",
      "    编号为 2\n",
      "目标单词：\n",
      "       对应编号为：       2   对应的语境单词为:        酒    编号为 40\n",
      "目标单词：酒       对应编号为：       40   对应的语境单词为:        \n",
      "    编号为 2\n",
      "目标单词：酒       对应编号为：       40   对应的语境单词为:        泉    编号为 613\n"
     ]
    }
   ],
   "source": [
    "#5.生成Word2Vec的训练样本，使用skip-gram模式\n",
    "data_index = 0\n",
    "\n",
    "def generate_batch(batch_size,num_skips,skip_window):\n",
    "    \"\"\"\n",
    "\n",
    "    :param batch_size: 每个训练批次的数据量\n",
    "    :param num_skips: 每个单词生成的样本数量，不能超过skip_window的两倍，并且必须是batch_size的整数倍\n",
    "    :param skip_window: 单词最远可以联系的距离，设置为1则表示当前单词只考虑前后两个单词之间的关系，也称为滑窗的大小\n",
    "    :return:返回每个批次的样本以及对应的标签\n",
    "    \"\"\"\n",
    "    global data_index #声明为全局变量，方便后期多次使用\n",
    "    #使用Python中的断言函数，提前对输入的参数进行判别，防止后期出bug而难以寻找原因\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= skip_window * 2\n",
    "\n",
    "    batch = np.ndarray(shape=(batch_size),dtype=np.int32) #创建一个batch_size大小的数组，数据类型为int32类型，数值随机\n",
    "    labels = np.ndarray(shape=(batch_size,1),dtype=np.int32) #数据维度为[batch_size,1]\n",
    "    span = 2 * skip_window + 1 #入队的长度\n",
    "    buffer = collections.deque(maxlen=span) #创建双向队列。最大长度为span\n",
    "    \"\"\"\n",
    "    print(batch,\"\\n\",labels) \n",
    "    batch :[0 ,-805306368  ,405222565 ,1610614781 ,-2106392574 ,2721-2106373584 ,163793]\n",
    "    labels: [[         0]\n",
    "            [-805306368]\n",
    "            [ 407791039]\n",
    "            [ 536872957]\n",
    "            [         2]\n",
    "            [         0]\n",
    "            [         0]\n",
    "            [    131072]]\n",
    "    \"\"\"\n",
    "    #对双向队列填入初始值\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index  = (data_index+1) % len(data)\n",
    "    \"\"\"\n",
    "    print(buffer,\"\\n\",data_index)  输出：\n",
    "                                    deque([5234, 3081, 12], maxlen=3) \n",
    "                                    3\n",
    "    \"\"\"\n",
    "    #进入第一层循环，i表示第几次入双向队列\n",
    "    for i in range(batch_size // num_skips):\n",
    "        target = skip_window #定义buffer中第skip_window个单词是目标\n",
    "        targets_avoid = [skip_window] #定义生成样本时需要避免的单词，因为我们要预测的是语境单词，不包括目标单词本身，因此列表开始包括第skip_window个单词\n",
    "        for j in range(num_skips):\n",
    "            \"\"\"第二层循环，每次循环对一个语境单词生成样本，先产生随机数，直到不在需要避免的单词中，也即需要找到可以使用的语境词语\"\"\"\n",
    "            while target in targets_avoid:\n",
    "                target = random.randint(0,span-1)\n",
    "            targets_avoid.append(target) #因为该语境单词已经被使用过了，因此将其添加到需要避免的单词库中\n",
    "            batch[i * num_skips + j] = buffer[skip_window] #目标词汇\n",
    "            labels[i * num_skips +j,0] = buffer[target] #语境词汇\n",
    "        #此时buffer已经填满，后续的数据会覆盖掉前面的数据\n",
    "        #print(batch,labels)\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    return batch,labels\n",
    "batch,labels = generate_batch(8,2,1)\n",
    "for i in range(8):\n",
    "    print(\"目标单词：\"+reverse_dictionary[batch[i]]+\"对应编号为：\".center(20)+str(batch[i])+\"   对应的语境单词为: \".ljust(20)+reverse_dictionary[labels[i,0]]+\"    编号为\",labels[i,0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#6.定义训练数据的一些参数\n",
    "batch_size = 128 #训练样本的批次大小\n",
    "embedding_size = 128 #单词转化为稠密词向量的维度\n",
    "skip_window = 1 #单词可以联系到的最远距离\n",
    "num_skips = 1 #每个目标单词提取的样本数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#7.定义验证数据的一些参数\n",
    "valid_size = 16 #验证的单词数\n",
    "valid_window = 100 #指验证单词只从频数最高的前100个单词中进行抽取\n",
    "valid_examples = np.random.choice(valid_window,valid_size,replace=False) #进行随机抽取\n",
    "num_sampled = 64 #训练时用来做负样本的噪声单词的数量\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#8.开始定义Skip-Gram Word2Vec模型的网络结构\n",
    "#8.1创建一个graph作为默认的计算图，同时为输入数据和标签申请占位符，并将验证样例的随机数保存成TensorFlow的常数\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    train_inputs = tf.placeholder(tf.int32,[batch_size])\n",
    "    train_labels = tf.placeholder(tf.int32,[batch_size,1])\n",
    "    valid_dataset = tf.constant(valid_examples,tf.int32)\n",
    "\n",
    "    #选择运行的device为CPU\n",
    "    with tf.device(\"/cpu:0\"):\n",
    "        #单词大小为50000，向量维度为128，随机采样在（-1，1）之间的浮点数\n",
    "        embeddings = tf.Variable(tf.random_uniform([vocabulary_size,embedding_size],-1.0,1.0))\n",
    "        #使用tf.nn.embedding_lookup()函数查找train_inputs对应的向量embed\n",
    "        embed = tf.nn.embedding_lookup(embeddings,train_inputs)\n",
    "\n",
    "        #优化目标选择NCE loss\n",
    "        #使用截断正太函数初始化NCE损失的权重,偏重初始化为0\n",
    "        nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size,embedding_size],stddev= 1.0 /math.sqrt(embedding_size)))\n",
    "        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "        #计算学习出的embedding在训练数据集上的loss，并使用tf.reduce_mean()函数进行汇总\n",
    "        loss = tf.reduce_mean(tf.nn.nce_loss(\n",
    "            weights=nce_weights,\n",
    "            biases=nce_biases,\n",
    "            labels =train_labels,\n",
    "            inputs=embed,\n",
    "            num_sampled=num_sampled,\n",
    "            num_classes=vocabulary_size\n",
    "        ))\n",
    "\n",
    "        #定义优化器为SGD，且学习率设置为1.0.然后计算嵌入向量embeddings的L2范数norm，并计算出标准化后的normalized_embeddings\n",
    "        optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n",
    "        norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims=True)) #嵌入向量的L2范数\n",
    "        normalized_embeddings = embeddings / norm #标准哈embeddings\n",
    "        valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,valid_dataset) #查询验证单词的嵌入向量\n",
    "        #计算验证单词的嵌入向量与词汇表中所有单词的相似性\n",
    "        similarity = tf.matmul(\n",
    "            valid_embeddings,normalized_embeddings,transpose_b=True\n",
    "        )\n",
    "        init = tf.global_variables_initializer() #定义参数的初始化\n",
    "        saver = tf.train.Saver()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "初始化完成！\n",
      "第0轮迭代后的损失为：208.32574462890625\n",
      "与单词 如 最相似的：  袂,  郫,  煦,  德,  阴,  刺,  六,  炽, \n",
      "与单词 金 最相似的：  拟,  储,  愔,  邛,  篷,  昌,  蛤,  托, \n",
      "与单词 未 最相似的：  因,  攧,  闱,  肇,  汁,  鹜,  韫,  鲁, \n",
      "与单词 年 最相似的：  半,  襦,  清,  ＊,  笃,  袂,  豫,  玲, \n",
      "与单词 小 最相似的：  裁,  冷,  褚,  蓿,  议,  损,  峡,  渌, \n",
      "与单词 今 最相似的：  擒,  牟,  态,  局,  坤,  毵,  煤,  牍, \n",
      "与单词 仙 最相似的：  鸦,  本,  貌,  遍,  扌,  迫,  帖,  犒, \n",
      "与单词 是 最相似的：  枇,  股,  骚,  窈,  巍,  痒,  炎,  朱, \n",
      "与单词 此 最相似的：  觊,  磅,  敬,  逶,  膏,  氵,  叵,  祝, \n",
      "与单词 似 最相似的：  希,  颁,  悴,  废,  翳,  踪,  廖,  夷, \n",
      "与单词 里 最相似的：  蹁,  璈,  谏,  绿,  蓖,  杨,  茶,  夸, \n",
      "与单词 心 最相似的：  葑,  雒,  戢,  地,  辱,  霭,  1,  蹇, \n",
      "与单词 行 最相似的：  彦,  几,  隐,  拏,  衤,  汾,  閟,  姚, \n",
      "与单词 重 最相似的：  旨,  昵,  箵,  鹘,  宸,  寿,  点,  供, \n",
      "与单词 知 最相似的：  涨,  烟,  拳,  濆,  怕,  贰,  两,  监, \n",
      "与单词 。 最相似的：  磷,  然,  舰,  笺,  卿,  撋,  撼,  隈, \n",
      "第2000轮迭代后的损失为：21.378977403879166\n",
      "第4000轮迭代后的损失为：5.233872623205185\n",
      "第6000轮迭代后的损失为：4.823450682997704\n",
      "第8000轮迭代后的损失为：4.675266424536705\n",
      "第10000轮迭代后的损失为：4.6531966471672055\n",
      "与单词 如 最相似的：  袂,  德,  隐,  拖,  郡,  珰,  六,  髻, \n",
      "与单词 金 最相似的：  篷,  拟,  储,  昌,  穿,  赤,  托,  缕, \n",
      "与单词 未 最相似的：  不,  因,  闱,  旆,  鹜,  鲁,  极,  砧, \n",
      "与单词 年 最相似的：   ,  营,  偷,  醑,  贱,  壮,  时,  半, \n",
      "与单词 小 最相似的：  冷,  峡,  议,  下,  渌,  损,  裁,  焕, \n",
      "与单词 今 最相似的：  坤,  态,  溜,  滋,  悠,  纱,  局,  牟, \n",
      "与单词 仙 最相似的：  本,  迫,  鸦,  貌,  帖,  秀,  遍,  冉, \n",
      "与单词 是 最相似的：  朱,  帽,  窈,   ,  枇,  骚,  儒,  巍, \n",
      "与单词 此 最相似的：  敬,  氵,  休,  膏,  琶,  祝,  蹴,  帆, \n",
      "与单词 似 最相似的：  翳,  悴,  希,  槛,  沧,  博,  踪,  颁, \n",
      "与单词 里 最相似的：  茶,  夸,  绿,  杨,  蹁,  栊,  璈,  谏, \n",
      "与单词 心 最相似的：  惭,  地,   ,  辱,  霭,  促,  模,  何, \n",
      "与单词 行 最相似的：  彦,  衤,  隐,  茶,  市,  几,  衔,  怜, \n",
      "与单词 重 最相似的：  旨,  昵,  寿,  整,  戟,  巧,  宸,  昌, \n",
      "与单词 知 最相似的：  怕,  苦,  博,  料,  两,  扬,  涨,  逐, \n",
      "与单词 。 最相似的：  ，,  、,  康,  \n",
      ",  拣,  ）,   ,  煮, \n",
      "第12000轮迭代后的损失为：4.508076788187027\n",
      "第14000轮迭代后的损失为：4.530160474300384\n",
      "第16000轮迭代后的损失为：4.3591042821407315\n",
      "第18000轮迭代后的损失为：4.33323743057251\n",
      "第20000轮迭代后的损失为：4.341415706455708\n",
      "与单词 如 最相似的：  似,  袂,  拖,  郡,  婴,  隐,  德,  阴, \n",
      "与单词 金 最相似的：  篷,  储,  愔,  拟,  穿,  昌,  缕,  赤, \n",
      "与单词 未 最相似的：  不,  因,  闱,  贮,  鹜,  难,  贰,  鲁, \n",
      "与单词 年 最相似的：   ,  时,  营,  贱,  醑,  壮,  偷,  缉, \n",
      "与单词 小 最相似的：  议,  冷,  峡,  渌,  损,  陡,  下,  焕, \n",
      "与单词 今 最相似的：  坤,  滋,  态,  溜,  唳,  悠,  去,  牟, \n",
      "与单词 仙 最相似的：  迫,  帖,  秀,  本,  超,  貌,  钝,  赣, \n",
      "与单词 是 最相似的：  帽,  窈,  儒,  到,   ,  朱,  桁,  恨, \n",
      "与单词 此 最相似的：  敬,  氵,  蹴,  休,  那,  终,  今,  叵, \n",
      "与单词 似 最相似的：  翳,  如,  博,  拦,  踪,  琢,  夺,  沧, \n",
      "与单词 里 最相似的：  茶,  蹁,  璈,  谏,  栊,  漾,  夸,  欣, \n",
      "与单词 心 最相似的：  惭,  地,  辱,  模,  促,  霭,   ,  葑, \n",
      "与单词 行 最相似的：  衤,  彦,  隐,  衔,  汾,  婷,  市,  茶, \n",
      "与单词 重 最相似的：  旨,  昵,  整,  寿,  柴,  摄,  巧,  偎, \n",
      "与单词 知 最相似的：  怕,  料,  复,  利,  苦,  扬,  博,  逢, \n",
      "与单词 。 最相似的：  ，,  、,  康,  拣,  裴,  评,  扆,  \n",
      ", \n",
      "第22000轮迭代后的损失为：4.31854377579689\n",
      "第24000轮迭代后的损失为：4.375219332933426\n",
      "第26000轮迭代后的损失为：4.355166732668876\n",
      "第28000轮迭代后的损失为：4.350904716730118\n",
      "第30000轮迭代后的损失为：4.262077564954757\n",
      "与单词 如 最相似的：  似,  拖,  宙,  袂,  婴,  。,  滔,  髟, \n",
      "与单词 金 最相似的：  玉,  愔,  拟,  谱,  篷,  储,  缕,  穿, \n",
      "与单词 未 最相似的：  不,  难,  因,  无,  贮,  鹜,  贰,  闱, \n",
      "与单词 年 最相似的：  时,  营,   ,  贱,  岁,  醑,  壮,  ＊, \n",
      "与单词 小 最相似的：  议,  冷,  峡,  陡,  渌,  蓿,  损,  韫, \n",
      "与单词 今 最相似的：  坤,  滋,  此,  态,  唳,  永,  牟,  诚, \n",
      "与单词 仙 最相似的：  迫,  秀,  本,  帖,  南,  超,  貌,  褚, \n",
      "与单词 是 最相似的：  儒,  到,  桁,  有,  窈,  帽,  恨,   , \n",
      "与单词 此 最相似的：  蹴,  敬,  今,  氵,  终,  奎,  琚,  叵, \n",
      "与单词 似 最相似的：  如,  拦,  夺,  翳,  踪,  琢,  魂,  博, \n",
      "与单词 里 最相似的：  茶,  蹁,  栊,  谏,  漾,  璈,  欣,  愧, \n",
      "与单词 心 最相似的：  惭,  辱,  情,  促,  地,  霭,  模,  料, \n",
      "与单词 行 最相似的：  衤,  彦,  衔,  婷,  汾,  隐,  拏,  茶, \n",
      "与单词 重 最相似的：  旨,  整,  昵,  偎,  摄,  柴,  关,  寿, \n",
      "与单词 知 最相似的：  怕,  料,  利,  复,  拳,  博,  羡,  苦, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  拣,  菩,  鹧,  扆,  康, \n",
      "第32000轮迭代后的损失为：4.24534375846386\n",
      "第34000轮迭代后的损失为：4.217204894661903\n",
      "第36000轮迭代后的损失为：4.239414916992187\n",
      "第38000轮迭代后的损失为：4.292895170927047\n",
      "第40000轮迭代后的损失为：4.290711967349052\n",
      "与单词 如 最相似的：  似,  宙,  婴,  拖,  滔,  在,  袂,  稀, \n",
      "与单词 金 最相似的：  玉,  愔,  谱,  储,  拟,  篷,  谙,  宝, \n",
      "与单词 未 最相似的：  不,  难,  已,  贮,  贰,  因,  劳,  砧, \n",
      "与单词 年 最相似的：  时,  岁,  贱,  营,  醑,  壮,  ＊,   , \n",
      "与单词 小 最相似的：  议,  冷,  渌,  蓿,  峡,  陡,  韫,  损, \n",
      "与单词 今 最相似的：  坤,  滋,  此,  诚,  唳,  鄞,  悠,  巅, \n",
      "与单词 仙 最相似的：  秀,  迫,  帖,  本,  超,  南,  褚,  赣, \n",
      "与单词 是 最相似的：  到,  儒,  桁,  有,  窈,  帽,  在,  恨, \n",
      "与单词 此 最相似的：  蹴,  今,  氵,  奎,  终,  敬,  叵,  朗, \n",
      "与单词 似 最相似的：  如,  拦,  翳,  琢,  在,  夺,  到,  老, \n",
      "与单词 里 最相似的：  茶,  蹁,  栊,  入,  欣,  漾,  谏,  愧, \n",
      "与单词 心 最相似的：  惭,  情,  辱,  促,  地,  料,  礼,  霭, \n",
      "与单词 行 最相似的：  彦,  衤,  汾,  衔,  拏,  婷,  隐,  侑, \n",
      "与单词 重 最相似的：  旨,  整,  偎,  摄,  昵,  看,  新,  关, \n",
      "与单词 知 最相似的：  怕,  利,  羡,  博,  料,  复,  拳,  苦, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  ）,  拣,  鹧,  蓦, \n",
      "第42000轮迭代后的损失为：4.258452712059021\n",
      "第44000轮迭代后的损失为：4.243667605400085\n",
      "第46000轮迭代后的损失为：4.183232167005539\n",
      "第48000轮迭代后的损失为：4.1665104078054425\n",
      "第50000轮迭代后的损失为：4.1814029448628425\n",
      "与单词 如 最相似的：  似,  宙,  婴,  在,  稀,  髟,  滔,  拖, \n",
      "与单词 金 最相似的：  玉,  宝,  愔,  谙,  谱,  瓷,  拟,  琼, \n",
      "与单词 未 最相似的：  不,  难,  已,  贰,  贮,  初,  乍,  砧, \n",
      "与单词 年 最相似的：  岁,  时,  贱,  营,  醑,  ＊,  壮,  琲, \n",
      "与单词 小 最相似的：  议,  蓿,  冷,  闲,  陡,  损,  掏,  渌, \n",
      "与单词 今 最相似的：  此,  坤,  永,  滋,  娩,  鄞,  诚,  避, \n",
      "与单词 仙 最相似的：  秀,  迫,  帖,  超,  本,  南,  褚,  赣, \n",
      "与单词 是 最相似的：  桁,  有,  到,  儒,  在,  帽,  恨,  窈, \n",
      "与单词 此 最相似的：  蹴,  今,  奎,  终,  叵,  敬,  朗,  氵, \n",
      "与单词 似 最相似的：  如,  在,  到,  拦,  琢,  翳,  知,  是, \n",
      "与单词 里 最相似的：  蹁,  茶,  入,  欣,  栊,  愧,  圃,  斫, \n",
      "与单词 心 最相似的：  情,  惭,  辱,  促,  料,  1,  泼,  韦, \n",
      "与单词 行 最相似的：  彦,  衤,  衔,  汾,  拏,  侑,  婷,  隐, \n",
      "与单词 重 最相似的：  偎,  旨,  整,  对,  看,  关,  摄,  新, \n",
      "与单词 知 最相似的：  怕,  羡,  料,  利,  复,  博,  拳,  由, \n",
      "与单词 。 最相似的：  ，,  、,  拣,  菩,  鹧,  \n",
      ",  砢,  扆, \n",
      "第52000轮迭代后的损失为：4.217059890747071\n",
      "第54000轮迭代后的损失为：4.2584612210989\n",
      "第56000轮迭代后的损失为：4.2142294716835025\n",
      "第58000轮迭代后的损失为：4.241247202038765\n",
      "第60000轮迭代后的损失为：4.120367769122124\n",
      "与单词 如 最相似的：  似,  宙,  在,  夭,  滔,  髟,  拖,  婴, \n",
      "与单词 金 最相似的：  玉,  宝,  谙,  愔,  梨,  谱,  拟,  瓷, \n",
      "与单词 未 最相似的：  不,  难,  已,  贮,  乍,  贰,  初,  无, \n",
      "与单词 年 最相似的：  岁,  贱,  营,  时,  醑,  少,  ＊,  琲, \n",
      "与单词 小 最相似的：  蓿,  议,  闲,  画,  陡,  杏,  掏,  冷, \n",
      "与单词 今 最相似的：  此,  坤,  永,  滋,  娩,  絇,  鄞,  疆, \n",
      "与单词 仙 最相似的：  秀,  帖,  迫,  超,  南,  本,  褚,  赣, \n",
      "与单词 是 最相似的：  有,  到,  桁,  在,  儒,  恨,  帽,  似, \n",
      "与单词 此 最相似的：  今,  蹴,  奎,  其,  叵,  终,  敬,  何, \n",
      "与单词 似 最相似的：  如,  在,  是,  到,  、,  拦,  知,  琢, \n",
      "与单词 里 最相似的：  蹁,  茶,  入,  栊,  欣,  圃,  愧,  坼, \n",
      "与单词 心 最相似的：  情,  惭,  促,  辱,  泼,  1,  禊,  韦, \n",
      "与单词 行 最相似的：  衤,  彦,  衔,  汾,  拏,  侑,  婷,  住, \n",
      "与单词 重 最相似的：  偎,  旨,  看,  整,  对,  瀑,  关,  新, \n",
      "与单词 知 最相似的：  羡,  料,  利,  拳,  博,  信,  由,  复, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  拣,  鹧,  奋,  砢, \n",
      "第62000轮迭代后的损失为：4.129988268256187\n",
      "第64000轮迭代后的损失为：4.148129300236702\n",
      "第66000轮迭代后的损失为：4.159883864760399\n",
      "第68000轮迭代后的损失为：4.223694816589355\n",
      "第70000轮迭代后的损失为：4.185241396069527\n",
      "与单词 如 最相似的：  似,  宙,  滔,  在,  夭,  婴,  稀,  髟, \n",
      "与单词 金 最相似的：  宝,  玉,  谙,  愔,  瓷,  琼,  穹,  谱, \n",
      "与单词 未 最相似的：  不,  难,  已,  乍,  贰,  初,  贮,  劳, \n",
      "与单词 年 最相似的：  岁,  贱,  营,  时,  醑,  琲,  ＊,  番, \n",
      "与单词 小 最相似的：  议,  闲,  蓿,  掏,  画,  陡,  <,  损, \n",
      "与单词 今 最相似的：  此,  坤,  娩,  滋,  永,  疆,  鄞,  避, \n",
      "与单词 仙 最相似的：  帖,  迫,  秀,  超,  本,  褚,  朅,  赣, \n",
      "与单词 是 最相似的：  有,  到,  桁,  在,  儒,  似,  恨,  奖, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  奎,  叵,  终,  敬,  朗, \n",
      "与单词 似 最相似的：  如,  在,  是,  到,  知,  为,  问,  、, \n",
      "与单词 里 最相似的：  入,  蹁,  茶,  栊,  中,  愧,  圃,  洲, \n",
      "与单词 心 最相似的：  情,  惭,  韦,  禊,  促,  泼,  没,  辱, \n",
      "与单词 行 最相似的：  衤,  彦,  拏,  衔,  汾,  住,  侑,  辇, \n",
      "与单词 重 最相似的：  偎,  对,  旨,  关,  看,  整,  瀑,  侃, \n",
      "与单词 知 最相似的：  羡,  由,  料,  酎,  信,  许,  利,  博, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  拣,  砢,  ）,  鹧, \n",
      "第72000轮迭代后的损失为：4.199180950403213\n",
      "第74000轮迭代后的损失为：4.11567981338501\n",
      "第76000轮迭代后的损失为：4.110088886737824\n",
      "第78000轮迭代后的损失为：4.120437555849552\n",
      "第80000轮迭代后的损失为：4.124685169696808\n",
      "与单词 如 最相似的：  似,  宙,  在,  拖,  夭,  为,  稀,  婴, \n",
      "与单词 金 最相似的：  宝,  玉,  瓷,  谙,  琼,  愔,  貂,  茁, \n",
      "与单词 未 最相似的：  不,  难,  已,  乍,  初,  贰,  先,  贮, \n",
      "与单词 年 最相似的：  岁,  时,  贱,  营,  琲,  ＊,  醑,  番, \n",
      "与单词 小 最相似的：  议,  掏,  蓿,  闲,  嫩,  赚,  画,  陡, \n",
      "与单词 今 最相似的：  此,  永,  娩,  坤,  滋,  疆,  避,  诚, \n",
      "与单词 仙 最相似的：  帖,  迫,  秀,  超,  朅,  本,  旆,  褚, \n",
      "与单词 是 最相似的：  有,  到,  桁,  在,  似,  儒,  恨,  帽, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  奎,  叵,  终,  惟,  嘈, \n",
      "与单词 似 最相似的：  如,  在,  是,  到,  问,  知,  、,  为, \n",
      "与单词 里 最相似的：  入,  蹁,  中,  愧,  栊,  茶,  坼,  欣, \n",
      "与单词 心 最相似的：  情,  惭,  泼,  没,  促,  这,  禊,  辱, \n",
      "与单词 行 最相似的：  彦,  拏,  衔,  衤,  汾,  住,  侑,  畿, \n",
      "与单词 重 最相似的：  偎,  对,  关,  瀑,  缬,  看,  繇,  僵, \n",
      "与单词 知 最相似的：  羡,  信,  许,  酎,  由,  料,  某,  博, \n",
      "与单词 。 最相似的：  ，,  、,  菩,  \n",
      ",  鹧,  2,  拣,  砢, \n",
      "第82000轮迭代后的损失为：4.186888358950615\n",
      "第84000轮迭代后的损失为：4.1906660560369495\n",
      "第86000轮迭代后的损失为：4.169016414403916\n",
      "第88000轮迭代后的损失为：4.115656383633613\n",
      "第90000轮迭代后的损失为：4.099169038176536\n",
      "与单词 如 最相似的：  似,  宙,  为,  夭,  在,  拖,  非,  做, \n",
      "与单词 金 最相似的：  宝,  玉,  谙,  茁,  琼,  愔,  闭,  瓷, \n",
      "与单词 未 最相似的：  不,  难,  已,  乍,  初,  贰,  先,  无, \n",
      "与单词 年 最相似的：  岁,  时,  ＊,  营,  贱,  琲,  壮,  醑, \n",
      "与单词 小 最相似的：  画,  蓿,  掏,  议,  赚,  闲,  杏,  嫩, \n",
      "与单词 今 最相似的：  此,  永,  坤,  娩,  何,  诚,  避,  絇, \n",
      "与单词 仙 最相似的：  帖,  迫,  朅,  秀,  超,  南,  褚,  本, \n",
      "与单词 是 最相似的：  有,  在,  桁,  到,  似,  恨,  儒,  过, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  叵,  奎,  屡,  终,  惟, \n",
      "与单词 似 最相似的：  如,  在,  是,  为,  问,  到,  知,  亘, \n",
      "与单词 里 最相似的：  入,  蹁,  中,  栊,  坼,  洲,  愧,  欣, \n",
      "与单词 心 最相似的：  情,  惭,  泼,  没,  这,  促,  禊,  辱, \n",
      "与单词 行 最相似的：  汾,  住,  拏,  衔,  彦,  衤,  畿,  侑, \n",
      "与单词 重 最相似的：  偎,  缬,  关,  侃,  对,  看,  端,  繇, \n",
      "与单词 知 最相似的：  羡,  信,  酎,  某,  由,  博,  料,  许, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  砢,  鹧,  拣,  炜, \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第92000轮迭代后的损失为：4.0737171882390975\n",
      "第94000轮迭代后的损失为：4.105336465477944\n",
      "第96000轮迭代后的损失为：4.157221971988678\n",
      "第98000轮迭代后的损失为：4.163519610166549\n",
      "第100000轮迭代后的损失为：4.139030213236809\n",
      "与单词 如 最相似的：  似,  宙,  夭,  在,  拖,  做,  非,  为, \n",
      "与单词 金 最相似的：  宝,  玉,  谙,  瓷,  茁,  梨,  貂,  愔, \n",
      "与单词 未 最相似的：  不,  难,  乍,  已,  初,  贰,  先,  贮, \n",
      "与单词 年 最相似的：  岁,  时,  营,  贱,  少,  ＊,  琲,  壮, \n",
      "与单词 小 最相似的：  画,  掏,  蓿,  赚,  陡,  嫩,  议,  杏, \n",
      "与单词 今 最相似的：  此,  来,  诚,  娩,  何,  坤,  避,  永, \n",
      "与单词 仙 最相似的：  帖,  迫,  朅,  秀,  本,  旆,  超,  谛, \n",
      "与单词 是 最相似的：  在,  有,  桁,  到,  似,  恨,  儒,  惬, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  叵,  终,  奎,  惟,  屡, \n",
      "与单词 似 最相似的：  如,  在,  是,  亘,  问,  忆,  记,  到, \n",
      "与单词 里 最相似的：  蹁,  入,  中,  洲,  愧,  在,  栊,  圃, \n",
      "与单词 心 最相似的：  情,  没,  泼,  这,  惭,  禊,  韦,  辱, \n",
      "与单词 行 最相似的：  彦,  拏,  住,  衔,  衤,  侑,  畿,  汾, \n",
      "与单词 重 最相似的：  偎,  缬,  侃,  端,  敝,  繇,  看,  关, \n",
      "与单词 知 最相似的：  信,  羡,  酎,  由,  许,  料,  博,  某, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  ）,  鹧,  扆,  緺, \n",
      "第102000轮迭代后的损失为：4.137242733120918\n",
      "第104000轮迭代后的损失为：4.067787682056427\n",
      "第106000轮迭代后的损失为：4.064173089504242\n",
      "第108000轮迭代后的损失为：4.069542679309845\n",
      "第110000轮迭代后的损失为：4.119831395268441\n",
      "与单词 如 最相似的：  似,  宙,  夭,  在,  于,  稀,  髟,  非, \n",
      "与单词 金 最相似的：  宝,  玉,  谙,  瓷,  茁,  梨,  银,  雕, \n",
      "与单词 未 最相似的：  不,  难,  乍,  已,  初,  先,  贰,  玦, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  壮,  营,  ＊,  日,  贱, \n",
      "与单词 小 最相似的：  掏,  画,  闲,  议,  蓿,  嫩,  陡,  杏, \n",
      "与单词 今 最相似的：  此,  来,  娩,  永,  诚,  避,  何,  絇, \n",
      "与单词 仙 最相似的：  帖,  迫,  朅,  本,  超,  旆,  秀,  棺, \n",
      "与单词 是 最相似的：  在,  有,  似,  桁,  到,  恨,  帽,  惬, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  叵,  屡,  终,  奎,  惟, \n",
      "与单词 似 最相似的：  如,  在,  是,  问,  为,  亘,  与,  到, \n",
      "与单词 里 最相似的：  中,  蹁,  入,  在,  愧,  坼,  栊,  叠, \n",
      "与单词 心 最相似的：  情,  泼,  这,  没,  惭,  禊,  筒,  辱, \n",
      "与单词 行 最相似的：  拏,  彦,  程,  住,  侑,  畿,  汾,  辇, \n",
      "与单词 重 最相似的：  偎,  缬,  端,  关,  侃,  对,  还,  看, \n",
      "与单词 知 最相似的：  信,  羡,  酎,  许,  由,  料,  某,  似, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  鹧,  ）,  砢,  緺, \n",
      "第112000轮迭代后的损失为：4.1572072224617\n",
      "第114000轮迭代后的损失为：4.1139606132507325\n",
      "第116000轮迭代后的损失为：4.146620990157127\n",
      "第118000轮迭代后的损失为：4.040827976226806\n",
      "第120000轮迭代后的损失为：4.048096682429314\n",
      "与单词 如 最相似的：  似,  宙,  夭,  于,  为,  髟,  滔,  非, \n",
      "与单词 金 最相似的：  宝,  玉,  谙,  茁,  瓷,  琼,  瑶,  银, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  初,  贰,  顿,  先, \n",
      "与单词 年 最相似的：  岁,  时,  壮,  番,  ＊,  琲,  日,  少, \n",
      "与单词 小 最相似的：  画,  掏,  蓿,  议,  闲,  赚,  杏,  陡, \n",
      "与单词 今 最相似的：  此,  来,  永,  何,  絇,  鄞,  娩,  诚, \n",
      "与单词 仙 最相似的：  帖,  朅,  迫,  本,  旆,  褚,  秀,  超, \n",
      "与单词 是 最相似的：  有,  在,  到,  似,  桁,  恨,  帽,  惬, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  叵,  终,  屡,  惟,  嘈, \n",
      "与单词 似 最相似的：  如,  在,  是,  问,  亘,  为,  知,  与, \n",
      "与单词 里 最相似的：  入,  中,  蹁,  愧,  坼,  壑,  欣,  栊, \n",
      "与单词 心 最相似的：  情,  这,  泼,  没,  惭,  筒,  禊,  促, \n",
      "与单词 行 最相似的：  拏,  住,  彦,  程,  辇,  侑,  畿,  汾, \n",
      "与单词 重 最相似的：  偎,  缬,  还,  侃,  端,  关,  看,  闭, \n",
      "与单词 知 最相似的：  信,  羡,  许,  酎,  由,  问,  似,  某, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  砢,  萨,  2,  鹧, \n",
      "第122000轮迭代后的损失为：4.069526283740998\n",
      "第124000轮迭代后的损失为：4.072877807617187\n",
      "第126000轮迭代后的损失为：4.142793106198311\n",
      "第128000轮迭代后的损失为：4.113716062426567\n",
      "第130000轮迭代后的损失为：4.117509055256844\n",
      "与单词 如 最相似的：  似,  宙,  于,  夭,  非,  为,  髟,  在, \n",
      "与单词 金 最相似的：  宝,  玉,  谙,  茁,  瓷,  雕,  瑶,  鳊, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  初,  贰,  先,  无, \n",
      "与单词 年 最相似的：  岁,  时,  壮,  琲,  少,  日,  番,  ＊, \n",
      "与单词 小 最相似的：  画,  掏,  赚,  蓿,  杏,  议,  嫩,  闲, \n",
      "与单词 今 最相似的：  此,  来,  何,  娩,  永,  絇,  诚,  终, \n",
      "与单词 仙 最相似的：  帖,  迫,  朅,  褚,  超,  本,  秀,  赣, \n",
      "与单词 是 最相似的：  有,  在,  似,  到,  桁,  惬,  恨,  铎, \n",
      "与单词 此 最相似的：  今,  蹴,  终,  其,  叵,  屡,  嘈,  法, \n",
      "与单词 似 最相似的：  如,  是,  在,  为,  问,  知,  亘,  与, \n",
      "与单词 里 最相似的：  入,  蹁,  中,  坼,  洲,  欣,  壑,  栊, \n",
      "与单词 心 最相似的：  情,  这,  没,  筒,  泼,  矧,  禊,  惭, \n",
      "与单词 行 最相似的：  彦,  住,  辇,  侑,  拏,  程,  畿,  汾, \n",
      "与单词 重 最相似的：  还,  偎,  端,  敝,  缬,  侃,  关,  剩, \n",
      "与单词 知 最相似的：  信,  羡,  许,  酎,  由,  问,  似,  识, \n",
      "与单词 。 最相似的：  ，,  、,  ）,  \n",
      ",  鹧,  菩,  扆,  砢, \n",
      "第132000轮迭代后的损失为：4.0514192913770675\n",
      "第134000轮迭代后的损失为：4.0381040424108505\n",
      "第136000轮迭代后的损失为：4.037887992799282\n",
      "第138000轮迭代后的损失为：4.05819923377037\n",
      "第140000轮迭代后的损失为：4.122867257595062\n",
      "与单词 如 最相似的：  似,  宙,  在,  于,  为,  非,  夭,  惟, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  瓷,  谙,  梨,  鳊, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  初,  贰,  砧, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  ＊,  贱,  壮,  番,  醑, \n",
      "与单词 小 最相似的：  掏,  画,  议,  赚,  儿,  杏,  蓿,  嫩, \n",
      "与单词 今 最相似的：  此,  娩,  永,  来,  诚,  何,  终,  备, \n",
      "与单词 仙 最相似的：  帖,  迫,  朅,  旆,  褚,  超,  棺,  冶, \n",
      "与单词 是 最相似的：  有,  在,  似,  桁,  到,  过,  向,  铎, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  叵,  终,  屡,  嘈,  惟, \n",
      "与单词 似 最相似的：  如,  在,  是,  为,  忆,  问,  亘,  知, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  栊,  洲,  壑,  坼,  愧, \n",
      "与单词 心 最相似的：  情,  这,  没,  筒,  泼,  禊,  矧,  促, \n",
      "与单词 行 最相似的：  程,  彦,  侑,  住,  拏,  汾,  辇,  8, \n",
      "与单词 重 最相似的：  还,  偎,  敝,  关,  端,  侃,  缬,  闭, \n",
      "与单词 知 最相似的：  信,  羡,  许,  酎,  识,  由,  问,  胆, \n",
      "与单词 。 最相似的：  ，,  、,  菩,  ）,  \n",
      ",  鹧,  緺,  砢, \n",
      "第142000轮迭代后的损失为：4.122255155682564\n",
      "第144000轮迭代后的损失为：4.0975150793790815\n",
      "第146000轮迭代后的损失为：4.053199625611305\n",
      "第148000轮迭代后的损失为：4.0450950351953505\n",
      "第150000轮迭代后的损失为：4.015444392204285\n",
      "与单词 如 最相似的：  似,  宙,  夭,  在,  于,  非,  惟,  髟, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  谙,  琼,  银,  鷃, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  贰,  欲,  无, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  ＊,  壮,  醑,  贱,  饶, \n",
      "与单词 小 最相似的：  掏,  画,  嫩,  儿,  杏,  蓿,  议,  赚, \n",
      "与单词 今 最相似的：  此,  诚,  娩,  终,  来,  何,  永,  备, \n",
      "与单词 仙 最相似的：  帖,  迫,  朅,  超,  驳,  真,  旆,  褚, \n",
      "与单词 是 最相似的：  在,  有,  似,  到,  桁,  铎,  向,  过, \n",
      "与单词 此 最相似的：  今,  其,  蹴,  叵,  屡,  嘈,  惟,  法, \n",
      "与单词 似 最相似的：  如,  在,  是,  知,  亘,  为,  与,  问, \n",
      "与单词 里 最相似的：  中,  蹁,  入,  栊,  坼,  壑,  洲,  摇, \n",
      "与单词 心 最相似的：  情,  这,  泼,  筒,  没,  禊,  惭,  矧, \n",
      "与单词 行 最相似的：  程,  侑,  辇,  拏,  汾,  住,  8,  彦, \n",
      "与单词 重 最相似的：  还,  关,  闭,  敝,  偎,  端,  缬,  侃, \n",
      "与单词 知 最相似的：  信,  羡,  许,  酎,  识,  似,  由,  问, \n",
      "与单词 。 最相似的：  ，,  、,  菩,  \n",
      ",  鹧,  2,  □,  砢, \n",
      "第152000轮迭代后的损失为：4.042249526858329\n",
      "第154000轮迭代后的损失为：4.09312989795208\n",
      "第156000轮迭代后的损失为：4.1104188871383665\n",
      "第158000轮迭代后的损失为：4.069739425420761\n",
      "第160000轮迭代后的损失为：4.088953438520432\n",
      "与单词 如 最相似的：  似,  宙,  夭,  于,  非,  为,  滔,  在, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  谙,  鳊,  雕,  鷃,  瓷, \n",
      "与单词 未 最相似的：  不,  已,  乍,  难,  无,  欲,  先,  贰, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  少,  ＊,  醑,  咀,  壮, \n",
      "与单词 小 最相似的：  掏,  画,  儿,  闲,  赚,  嫩,  蓿,  杏, \n",
      "与单词 今 最相似的：  此,  何,  诚,  终,  来,  娩,  备,  永, \n",
      "与单词 仙 最相似的：  帖,  朅,  迫,  真,  旆,  冶,  棺,  驳, \n",
      "与单词 是 最相似的：  有,  在,  似,  过,  铎,  到,  也,  向, \n",
      "与单词 此 最相似的：  今,  蹴,  其,  屡,  终,  法,  嘈,  叵, \n",
      "与单词 似 最相似的：  如,  在,  是,  知,  忆,  问,  为,  见, \n",
      "与单词 里 最相似的：  蹁,  入,  中,  坼,  栊,  壑,  洲,  欣, \n",
      "与单词 心 最相似的：  情,  筒,  这,  泼,  没,  矧,  禊,  惭, \n",
      "与单词 行 最相似的：  程,  辇,  住,  拏,  侑,  彦,  汾,  8, \n",
      "与单词 重 最相似的：  还,  端,  偎,  敝,  那,  剩,  繇,  缬, \n",
      "与单词 知 最相似的：  信,  羡,  似,  许,  识,  酎,  问,  由, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  鹧,  菩,  襜,  ）,  砢, \n",
      "第162000轮迭代后的损失为：4.010542867660522\n",
      "第164000轮迭代后的损失为：4.0199182841777805\n",
      "第166000轮迭代后的损失为：4.014526610910893\n",
      "第168000轮迭代后的损失为：4.071779165625572\n",
      "第170000轮迭代后的损失为：4.109683528542519\n",
      "与单词 如 最相似的：  似,  宙,  非,  夭,  为,  于,  拖,  在, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  谙,  瓷,  鳊,  琼, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  欲,  无,  先,  顿, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  ＊,  咀,  醑,  番,  饶, \n",
      "与单词 小 最相似的：  掏,  画,  儿,  闲,  蓿,  喔,  住,  斜, \n",
      "与单词 今 最相似的：  此,  诚,  何,  备,  终,  娩,  昨,  来, \n",
      "与单词 仙 最相似的：  帖,  朅,  迫,  冶,  棺,  旆,  真,  蓠, \n",
      "与单词 是 最相似的：  在,  有,  把,  向,  似,  桁,  过,  到, \n",
      "与单词 此 最相似的：  今,  其,  蹴,  屡,  嘈,  叵,  法,  且, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  问,  亘,  与,  知, \n",
      "与单词 里 最相似的：  蹁,  中,  入,  洲,  坼,  壑,  动,  栊, \n",
      "与单词 心 最相似的：  情,  这,  筒,  泼,  没,  矧,  禊,  惭, \n",
      "与单词 行 最相似的：  程,  辇,  侑,  彦,  拏,  住,  谊,  汾, \n",
      "与单词 重 最相似的：  还,  剩,  敝,  端,  繇,  那,  偎,  再, \n",
      "与单词 知 最相似的：  信,  羡,  识,  许,  酎,  胆,  敻,  努, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  鹧,  砢,  緺,  ）, \n",
      "第172000轮迭代后的损失为：4.073576809525489\n",
      "第174000轮迭代后的损失为：4.093454038619995\n",
      "第176000轮迭代后的损失为：3.9982743552923203\n",
      "第178000轮迭代后的损失为：4.0093184466361995\n",
      "第180000轮迭代后的损失为：4.016544643104076\n",
      "与单词 如 最相似的：  似,  宙,  于,  夭,  非,  为,  髟,  拖, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  瓷,  谙,  琼,  鷃, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  无,  初, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  ＊,  番,  饶,  壮,  日, \n",
      "与单词 小 最相似的：  掏,  儿,  画,  嫩,  闲,  斜,  赚,  陡, \n",
      "与单词 今 最相似的：  此,  终,  诚,  备,  娩,  何,  昨,  来, \n",
      "与单词 仙 最相似的：  帖,  朅,  真,  迫,  蓠,  棺,  冶,  旆, \n",
      "与单词 是 最相似的：  有,  在,  似,  向,  过,  到,  桁,  铎, \n",
      "与单词 此 最相似的：  今,  其,  屡,  蹴,  我,  法,  嘈,  且, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  与,  问,  亘,  为, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  坼,  摇,  壑,  洲,  动, \n",
      "与单词 心 最相似的：  情,  筒,  这,  没,  泼,  矧,  惭,  禊, \n",
      "与单词 行 最相似的：  辇,  程,  侑,  拏,  彦,  散,  谊,  住, \n",
      "与单词 重 最相似的：  还,  那,  端,  敝,  闭,  繇,  关,  再, \n",
      "与单词 知 最相似的：  信,  羡,  许,  识,  酎,  敻,  胆,  问, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  菩,  鹧,  砢,  緺, \n",
      "第182000轮迭代后的损失为：4.0231097627878185\n",
      "第184000轮迭代后的损失为：4.0965526067018505\n",
      "第186000轮迭代后的损失为：4.082179787397385\n",
      "第188000轮迭代后的损失为：4.0693038403987885\n",
      "第190000轮迭代后的损失为：4.013329598426819\n",
      "与单词 如 最相似的：  似,  宙,  于,  为,  非,  拖,  夭,  在, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  谙,  瓷,  鳊,  罍, \n",
      "与单词 未 最相似的：  不,  难,  乍,  已,  欲,  无,  先,  顿, \n",
      "与单词 年 最相似的：  岁,  ＊,  时,  琲,  番,  壮,  日,  饶, \n",
      "与单词 小 最相似的：  掏,  儿,  画,  斜,  蓿,  嫩,  闲,  赚, \n",
      "与单词 今 最相似的：  此,  备,  终,  诚,  娩,  昨,  来,  何, \n",
      "与单词 仙 最相似的：  帖,  朅,  真,  蓠,  迫,  冶,  旆,  棺, \n",
      "与单词 是 最相似的：  在,  有,  似,  到,  把,  向,  惬,  铎, \n",
      "与单词 此 最相似的：  今,  其,  蹴,  屡,  後,  我,  快,  终, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  问,  记,  为,  亘, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  坼,  壑,  栊,  闭,  动, \n",
      "与单词 心 最相似的：  情,  筒,  没,  这,  泼,  矧,  惭,  养, \n",
      "与单词 行 最相似的：  辇,  程,  住,  拏,  谊,  8,  营,  彦, \n",
      "与单词 重 最相似的：  还,  敝,  那,  再,  端,  叠,  繇,  剩, \n",
      "与单词 知 最相似的：  信,  羡,  识,  酎,  许,  敻,  似,  某, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  劬,  鹧,  是,  花, \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第192000轮迭代后的损失为：4.01248376595974\n",
      "第194000轮迭代后的损失为：3.987353961586952\n",
      "第196000轮迭代后的损失为：4.0141627243757245\n",
      "第198000轮迭代后的损失为：4.072551476240158\n",
      "第200000轮迭代后的损失为：4.080526960730553\n",
      "与单词 如 最相似的：  似,  于,  宙,  为,  非,  拖,  滔,  在, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  瓷,  谙,  鳊,  琼, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  欲,  易,  先,  顿, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  饶,  番,  日,  醑,  少, \n",
      "与单词 小 最相似的：  掏,  儿,  画,  侧,  危,  住,  赚,  喔, \n",
      "与单词 今 最相似的：  此,  何,  备,  娩,  诚,  终,  昨,  来, \n",
      "与单词 仙 最相似的：  帖,  朅,  蓠,  冶,  真,  棺,  迫,  旆, \n",
      "与单词 是 最相似的：  有,  在,  过,  把,  惬,  向,  到,  似, \n",
      "与单词 此 最相似的：  今,  屡,  其,  蹴,  我,  快,  後,  终, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  知,  为,  记,  亘, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  坼,  动,  壑,  洲,  闭, \n",
      "与单词 心 最相似的：  情,  筒,  这,  没,  昽,  泼,  矧,  惭, \n",
      "与单词 行 最相似的：  程,  辇,  彦,  侑,  营,  拏,  8,  谊, \n",
      "与单词 重 最相似的：  还,  剩,  再,  那,  敝,  闭,  偎,  端, \n",
      "与单词 知 最相似的：  信,  识,  羡,  许,  酎,  敻,  胆,  似, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  菩,  鹧,  緺,  鸪, \n",
      "第202000轮迭代后的损失为：4.058750442385674\n",
      "第204000轮迭代后的损失为：4.017854097366333\n",
      "第206000轮迭代后的损失为：4.0015454120635985\n",
      "第208000轮迭代后的损失为：3.9857933880090712\n",
      "第210000轮迭代后的损失为：4.002543667554855\n",
      "与单词 如 最相似的：  似,  宙,  夭,  于,  在,  拖,  非,  为, \n",
      "与单词 金 最相似的：  宝,  瓷,  茁,  玉,  雕,  谙,  鳊,  鷃, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  欲,  先,  顿,  无, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  壮,  □,  饶,  少,  ＊, \n",
      "与单词 小 最相似的：  掏,  儿,  蓿,  侧,  喔,  危,  画,  斜, \n",
      "与单词 今 最相似的：  此,  来,  备,  诚,  终,  娩,  昨,  永, \n",
      "与单词 仙 最相似的：  帖,  蓠,  朅,  迫,  真,  冶,  旆,  棺, \n",
      "与单词 是 最相似的：  有,  在,  把,  似,  向,  过,  到,  恨, \n",
      "与单词 此 最相似的：  今,  屡,  其,  快,  蹴,  终,  我,  後, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  胜,  辈,  待,  记, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  坼,  洲,  壑,  摇,  动, \n",
      "与单词 心 最相似的：  情,  筒,  矧,  这,  没,  惭,  养,  泼, \n",
      "与单词 行 最相似的：  程,  辇,  营,  侑,  谊,  甑,  脚,  畿, \n",
      "与单词 重 最相似的：  还,  再,  闭,  剩,  敝,  端,  关,  叠, \n",
      "与单词 知 最相似的：  信,  羡,  识,  敻,  许,  酎,  问,  胆, \n",
      "与单词 。 最相似的：  ，,  、,  鹧,  ）,  \n",
      ",  菩,  浣,  谒, \n",
      "第212000轮迭代后的损失为：4.045931575536728\n",
      "第214000轮迭代后的损失为：4.079967065930367\n",
      "第216000轮迭代后的损失为：4.036372800111771\n",
      "第218000轮迭代后的损失为：4.054742701411247\n",
      "第220000轮迭代后的损失为：3.9651821502447127\n",
      "与单词 如 最相似的：  似,  宙,  于,  为,  在,  拖,  非,  夭, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  瓷,  雕,  芘,  谙,  鳊, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  欲,  先,  易,  无, \n",
      "与单词 年 最相似的：  岁,  时,  少,  琲,  壮,  ＊,  番,  饶, \n",
      "与单词 小 最相似的：  儿,  掏,  画,  侧,  斜,  拥,  喔,  簇, \n",
      "与单词 今 最相似的：  此,  昨,  备,  来,  何,  诚,  终,  永, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  冶,  驳,  迫,  棺, \n",
      "与单词 是 最相似的：  在,  有,  似,  把,  向,  到,  惬,  过, \n",
      "与单词 此 最相似的：  今,  其,  屡,  我,  後,  终,  蹴,  快, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  记,  胜,  为,  亘, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  坼,  闭,  动,  摇,  洲, \n",
      "与单词 心 最相似的：  情,  矧,  没,  筒,  泼,  惭,  养,  愁, \n",
      "与单词 行 最相似的：  程,  辇,  营,  甑,  脚,  拏,  汾,  住, \n",
      "与单词 重 最相似的：  还,  闭,  再,  端,  叠,  剩,  舵,  关, \n",
      "与单词 知 最相似的：  信,  羡,  识,  敻,  酎,  许,  问,  似, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  砢,  ）,  鹧,  花, \n",
      "第222000轮迭代后的损失为：3.9945568256378174\n",
      "第224000轮迭代后的损失为：3.9811219106912614\n",
      "第226000轮迭代后的损失为：4.033151698112488\n",
      "第228000轮迭代后的损失为：4.065006371617317\n",
      "第230000轮迭代后的损失为：4.041583627939224\n",
      "与单词 如 最相似的：  似,  宙,  为,  于,  拖,  夭,  滔,  唏, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  瓷,  雕,  罍,  芘,  谙, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  欲,  顿,  易,  初, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  ＊,  壮,  番,  饶,  少, \n",
      "与单词 小 最相似的：  掏,  儿,  画,  侧,  斜,  竹,  簇,  喔, \n",
      "与单词 今 最相似的：  此,  来,  昨,  备,  终,  诚,  每,  何, \n",
      "与单词 仙 最相似的：  蓠,  帖,  冶,  朅,  棺,  真,  超,  驳, \n",
      "与单词 是 最相似的：  在,  有,  惬,  把,  向,  似,  过,  恨, \n",
      "与单词 此 最相似的：  今,  屡,  法,  快,  终,  後,  其,  蹴, \n",
      "与单词 似 最相似的：  如,  在,  胜,  忆,  是,  为,  知,  记, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  闭,  动,  壑,  坼,  摇, \n",
      "与单词 心 最相似的：  情,  筒,  矧,  没,  养,  这,  昽,  泼, \n",
      "与单词 行 最相似的：  辇,  营,  甑,  程,  鹿,  拏,  煦,  8, \n",
      "与单词 重 最相似的：  还,  再,  剩,  闭,  端,  那,  敝,  舵, \n",
      "与单词 知 最相似的：  信,  识,  羡,  敻,  酎,  许,  似,  努, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  砢,  ）,  菩,  鹧,  鸪, \n",
      "第232000轮迭代后的损失为：4.054681441545487\n",
      "第234000轮迭代后的损失为：3.9740442742109297\n",
      "第236000轮迭代后的损失为：3.973843616604805\n",
      "第238000轮迭代后的损失为：3.9945045313835146\n",
      "第240000轮迭代后的损失为：3.9959664942026136\n",
      "与单词 如 最相似的：  似,  于,  宙,  夭,  为,  躲,  拖,  在, \n",
      "与单词 金 最相似的：  宝,  茁,  玉,  瓷,  芘,  鳊,  雕,  溜, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  初,  易, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  日,  ＊,  番,  壮,  孰, \n",
      "与单词 小 最相似的：  掏,  儿,  画,  侧,  秾,  危,  喔,  竹, \n",
      "与单词 今 最相似的：  此,  来,  终,  昨,  备,  诚,  每,  何, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  棺,  冶,  真,  旆,  驳, \n",
      "与单词 是 最相似的：  有,  在,  把,  向,  到,  过,  铎,  似, \n",
      "与单词 此 最相似的：  今,  快,  屡,  其,  终,  法,  後,  偈, \n",
      "与单词 似 最相似的：  如,  在,  胜,  是,  忆,  为,  辈,  奈, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  闭,  壑,  摇,  救,  坼, \n",
      "与单词 心 最相似的：  情,  筒,  矧,  泼,  养,  这,  禊,  愁, \n",
      "与单词 行 最相似的：  辇,  甑,  营,  程,  侑,  谊,  煦,  拏, \n",
      "与单词 重 最相似的：  还,  再,  闭,  舵,  叠,  那,  端,  敝, \n",
      "与单词 知 最相似的：  信,  识,  羡,  敻,  许,  酎,  努,  问, \n",
      "与单词 。 最相似的：  ，,  、,  ）,  \n",
      ",  菩,  鹧,  緺,  鸪, \n",
      "第242000轮迭代后的损失为：4.049402642250061\n",
      "第244000轮迭代后的损失为：4.061732090234757\n",
      "第246000轮迭代后的损失为：4.036942339420318\n",
      "第248000轮迭代后的损失为：3.986650561928749\n",
      "第250000轮迭代后的损失为：3.9856428142786027\n",
      "与单词 如 最相似的：  似,  宙,  为,  于,  成,  夭,  嚎,  拖, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  频,  芘,  瓷,  罍, \n",
      "与单词 未 最相似的：  不,  已,  乍,  难,  欲,  先,  无,  易, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  壮,  少,  ＊,  番,  饶, \n",
      "与单词 小 最相似的：  掏,  儿,  画,  侧,  竹,  簇,  危,  斜, \n",
      "与单词 今 最相似的：  此,  昨,  终,  来,  它,  疆,  备,  诚, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  冶,  驳,  棺,  掞, \n",
      "与单词 是 最相似的：  在,  有,  把,  似,  向,  到,  惬,  铎, \n",
      "与单词 此 最相似的：  今,  快,  其,  终,  屡,  後,  法,  毕, \n",
      "与单词 似 最相似的：  如,  在,  是,  胜,  为,  辈,  忆,  知, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  闭,  坼,  壑,  摇,  动, \n",
      "与单词 心 最相似的：  情,  矧,  没,  这,  愁,  筒,  惭,  泼, \n",
      "与单词 行 最相似的：  辇,  营,  侑,  程,  甑,  散,  煦,  拏, \n",
      "与单词 重 最相似的：  还,  再,  闭,  端,  舵,  繇,  敝,  那, \n",
      "与单词 知 最相似的：  信,  识,  羡,  敻,  酎,  许,  似,  努, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  ）,  鹧,  劬,  砢, \n",
      "第252000轮迭代后的损失为：3.9596424630880356\n",
      "第254000轮迭代后的损失为：3.9860746278762815\n",
      "第256000轮迭代后的损失为：4.043669414043427\n",
      "第258000轮迭代后的损失为：4.05436335003376\n",
      "第260000轮迭代后的损失为：4.029670901060104\n",
      "与单词 如 最相似的：  似,  宙,  于,  为,  非,  夭,  在,  躲, \n",
      "与单词 金 最相似的：  宝,  玉,  雕,  茁,  瓷,  芘,  罍,  鳊, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  易,  无,  贰, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  ＊,  孰,  壮,  日,  少, \n",
      "与单词 小 最相似的：  掏,  儿,  簇,  侧,  画,  危,  喔,  斜, \n",
      "与单词 今 最相似的：  此,  昨,  终,  来,  它,  备,  疆,  每, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  棺,  冶,  旆,  驳, \n",
      "与单词 是 最相似的：  有,  在,  把,  向,  惬,  铎,  似,  也, \n",
      "与单词 此 最相似的：  今,  快,  屡,  其,  终,  法,  後,  偈, \n",
      "与单词 似 最相似的：  如,  在,  是,  忆,  胜,  记,  辈,  知, \n",
      "与单词 里 最相似的：  中,  蹁,  入,  闭,  坼,  摇,  动,  洲, \n",
      "与单词 心 最相似的：  情,  筒,  矧,  这,  鶒,  养,  愁,  预, \n",
      "与单词 行 最相似的：  辇,  营,  侑,  甑,  程,  谊,  散,  煦, \n",
      "与单词 重 最相似的：  还,  再,  舵,  那,  端,  敝,  闭,  叠, \n",
      "与单词 知 最相似的：  信,  识,  羡,  敻,  酎,  许,  努,  似, \n",
      "与单词 。 最相似的：  ，,  、,  ）,  菩,  \n",
      ",  鹧,  緺,  鸪, \n",
      "第262000轮迭代后的损失为：4.00166300535202\n",
      "第264000轮迭代后的损失为：3.981066220879555\n",
      "第266000轮迭代后的损失为：3.9533142807483674\n",
      "第268000轮迭代后的损失为：3.972454311668873\n",
      "第270000轮迭代后的损失为：4.01423012137413\n",
      "与单词 如 最相似的：  似,  于,  宙,  夭,  为,  冱,  在,  嚎, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  瓷,  芘,  鳊,  琼, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  无,  贰, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  孰,  壮,  ＊,  饶,  醑, \n",
      "与单词 小 最相似的：  掏,  儿,  危,  簇,  画,  侧,  拥,  赚, \n",
      "与单词 今 最相似的：  此,  终,  昨,  备,  它,  来,  疆,  吊, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  棺,  真,  噫,  冶,  旆, \n",
      "与单词 是 最相似的：  在,  有,  向,  把,  惬,  似,  过,  作, \n",
      "与单词 此 最相似的：  今,  快,  屡,  法,  後,  划,  偈,  毕, \n",
      "与单词 似 最相似的：  如,  在,  胜,  是,  记,  待,  知,  为, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  闭,  坼,  摇,  动,  壑, \n",
      "与单词 心 最相似的：  情,  筒,  矧,  这,  愁,  泼,  预,  没, \n",
      "与单词 行 最相似的：  营,  侑,  辇,  散,  程,  甑,  拏,  茶, \n",
      "与单词 重 最相似的：  还,  再,  端,  舵,  闭,  叠,  那,  敝, \n",
      "与单词 知 最相似的：  信,  识,  羡,  敻,  酎,  许,  努,  似, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  鹧,  ）,  緺,  谒, \n",
      "第272000轮迭代后的损失为：4.047959234118462\n",
      "第274000轮迭代后的损失为：4.028042523145675\n",
      "第276000轮迭代后的损失为：4.040582880020142\n",
      "第278000轮迭代后的损失为：3.9450378886461257\n",
      "第280000轮迭代后的损失为：3.969171772480011\n",
      "与单词 如 最相似的：  似,  于,  宙,  嚎,  夭,  拖,  在,  为, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  频,  芘,  雕,  瓷,  罍, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  贰,  初, \n",
      "与单词 年 最相似的：  岁,  时,  壮,  琲,  番,  ＊,  饶,  日, \n",
      "与单词 小 最相似的：  掏,  儿,  侧,  拥,  簇,  竹,  危,  画, \n",
      "与单词 今 最相似的：  昨,  此,  终,  它,  永,  备,  每,  昔, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  棺,  冶,  超,  驳, \n",
      "与单词 是 最相似的：  有,  在,  把,  似,  向,  过,  到,  惬, \n",
      "与单词 此 最相似的：  今,  快,  屡,  法,  终,  後,  也,  偈, \n",
      "与单词 似 最相似的：  如,  在,  是,  记,  待,  辈,  忆,  与, \n",
      "与单词 里 最相似的：  中,  入,  蹁,  闭,  壑,  坼,  摇,  洲, \n",
      "与单词 心 最相似的：  情,  愁,  筒,  矧,  意,  鶒,  这,  预, \n",
      "与单词 行 最相似的：  辇,  营,  程,  侑,  拏,  谊,  撩,  散, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  那,  闭,  敝,  端, \n",
      "与单词 知 最相似的：  信,  识,  酎,  羡,  许,  敻,  努,  似, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  砢,  鹧,  菩,  緺, \n",
      "第282000轮迭代后的损失为：3.961351988554001\n",
      "第284000轮迭代后的损失为：3.9993934096097945\n",
      "第286000轮迭代后的损失为：4.046832948923111\n",
      "第288000轮迭代后的损失为：4.0206135247945785\n",
      "第290000轮迭代后的损失为：4.028157603740692\n",
      "与单词 如 最相似的：  似,  于,  宙,  在,  冱,  夭,  非,  嚎, \n",
      "与单词 金 最相似的：  宝,  玉,  雕,  瓷,  茁,  芘,  逗,  瑶, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  贰,  先,  欲,  易, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  日,  饶,  记,  壮,  咀, \n",
      "与单词 小 最相似的：  侧,  掏,  儿,  簇,  拥,  画,  危,  斜, \n",
      "与单词 今 最相似的：  此,  昨,  终,  来,  它,  每,  昔,  备, \n",
      "与单词 仙 最相似的：  蓠,  帖,  棺,  朅,  真,  掞,  炁,  跻, \n",
      "与单词 是 最相似的：  在,  有,  把,  向,  惬,  负,  作,  似, \n",
      "与单词 此 最相似的：  今,  快,  屡,  法,  终,  後,  偈,  之, \n",
      "与单词 似 最相似的：  如,  在,  记,  是,  胜,  见,  忆,  辈, \n",
      "与单词 里 最相似的：  中,  蹁,  入,  摇,  闭,  坼,  洲,  壑, \n",
      "与单词 心 最相似的：  情,  筒,  矧,  愁,  预,  鶒,  养,  昽, \n",
      "与单词 行 最相似的：  辇,  营,  程,  侑,  甑,  散,  谊,  拏, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  端,  那,  敝,  繇, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  许,  羡,  努,  枳, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  ）,  鹧,  緺,  炜, \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第292000轮迭代后的损失为：3.9549404262304306\n",
      "第294000轮迭代后的损失为：3.9601255728006364\n",
      "第296000轮迭代后的损失为：3.967885943353176\n",
      "第298000轮迭代后的损失为：3.9726980048418046\n",
      "第300000轮迭代后的损失为：4.025101484060287\n",
      "与单词 如 最相似的：  似,  于,  宙,  在,  拖,  为,  谆,  冱, \n",
      "与单词 金 最相似的：  宝,  玉,  雕,  茁,  瓷,  芘,  逗,  罍, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  贰,  欲,  无, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  壮,  孰,  日,  ＊,  缦, \n",
      "与单词 小 最相似的：  掏,  儿,  侧,  簇,  危,  谯,  竹,  拥, \n",
      "与单词 今 最相似的：  终,  昨,  此,  备,  每,  它,  吊,  来, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  棺,  噫,  掞,  炁, \n",
      "与单词 是 最相似的：  在,  有,  把,  惬,  过,  向,  非,  似, \n",
      "与单词 此 最相似的：  今,  屡,  快,  法,  偈,  且,  终,  毕, \n",
      "与单词 似 最相似的：  如,  在,  记,  胜,  是,  待,  辈,  忆, \n",
      "与单词 里 最相似的：  中,  入,  闭,  蹁,  壑,  洲,  坼,  动, \n",
      "与单词 心 最相似的：  情,  愁,  筒,  矧,  匡,  预,  这,  鶒, \n",
      "与单词 行 最相似的：  辇,  营,  甑,  侑,  程,  散,  拏,  煦, \n",
      "与单词 重 最相似的：  还,  再,  叠,  繇,  敝,  那,  舵,  端, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  努,  许,  枳,  羡, \n",
      "与单词 。 最相似的：  ，,  \n",
      ",  、,  ）,  菩,  緺,  鹧,  谒, \n",
      "第302000轮迭代后的损失为：4.0502365267276765\n",
      "第304000轮迭代后的损失为：4.021029381036758\n",
      "第306000轮迭代后的损失为：3.95830938231945\n",
      "第308000轮迭代后的损失为：3.972623283147812\n",
      "第310000轮迭代后的损失为：3.935641392767429\n",
      "与单词 如 最相似的：  似,  宙,  于,  拖,  夭,  冱,  嚎,  在, \n",
      "与单词 金 最相似的：  宝,  玉,  雕,  茁,  芘,  瓷,  罍,  频, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  初,  贰,  欲, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  壮,  记,  饶,  □,  日, \n",
      "与单词 小 最相似的：  儿,  掏,  侧,  簇,  拥,  危,  谯,  竹, \n",
      "与单词 今 最相似的：  昨,  此,  它,  终,  备,  永,  来,  每, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  棺,  掞,  跻,  炁, \n",
      "与单词 是 最相似的：  在,  有,  把,  向,  似,  恐,  个,  惬, \n",
      "与单词 此 最相似的：  今,  屡,  快,  且,  後,  法,  我,  惟, \n",
      "与单词 似 最相似的：  如,  在,  胜,  是,  待,  记,  亦,  奈, \n",
      "与单词 里 最相似的：  中,  入,  与,  闭,  蹁,  坼,  摇,  壑, \n",
      "与单词 心 最相似的：  情,  愁,  鶒,  筒,  矧,  意,  预,  养, \n",
      "与单词 行 最相似的：  辇,  散,  侑,  营,  谊,  程,  拏,  甑, \n",
      "与单词 重 最相似的：  还,  再,  叠,  □,  敝,  端,  繇,  同, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  认,  许,  努,  羡, \n",
      "与单词 。 最相似的：  ，,  、,  菩,  緺,  砢,  ）,  \n",
      ",  抨, \n",
      "第312000轮迭代后的损失为：3.9633489025831223\n",
      "第314000轮迭代后的损失为：4.022000125765801\n",
      "第316000轮迭代后的损失为：4.031433575630188\n",
      "第318000轮迭代后的损失为：4.003071155428886\n",
      "第320000轮迭代后的损失为：3.9890901477336884\n",
      "与单词 如 最相似的：  似,  宙,  于,  夭,  在,  冱,  拖,  曝, \n",
      "与单词 金 最相似的：  玉,  宝,  雕,  芘,  罍,  逗,  茁,  瓷, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  先,  无,  欲,  能, \n",
      "与单词 年 最相似的：  岁,  时,  少,  琲,  壮,  日,  饶,  ＊, \n",
      "与单词 小 最相似的：  儿,  掏,  侧,  簇,  斜,  画,  危,  拥, \n",
      "与单词 今 最相似的：  昨,  此,  终,  永,  备,  它,  何,  每, \n",
      "与单词 仙 最相似的：  蓠,  帖,  朅,  真,  炁,  棺,  驳,  跻, \n",
      "与单词 是 最相似的：  在,  有,  把,  作,  似,  向,  惬,  负, \n",
      "与单词 此 最相似的：  今,  快,  後,  屡,  终,  偈,  嘈,  且, \n",
      "与单词 似 最相似的：  如,  在,  是,  记,  见,  胜,  亦,  知, \n",
      "与单词 里 最相似的：  中,  闭,  蹁,  入,  与,  坼,  户,  洲, \n",
      "与单词 心 最相似的：  情,  愁,  矧,  筒,  鶒,  养,  没,  害, \n",
      "与单词 行 最相似的：  辇,  营,  程,  侑,  谊,  甑,  散,  拏, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  端,  繇,  剩,  那, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  羡,  努,  许,  认, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  花,  ）,  鸪,  砢,  劬, \n",
      "第322000轮迭代后的损失为：3.9559720939397813\n",
      "第324000轮迭代后的损失为：3.9404420697689058\n",
      "第326000轮迭代后的损失为：3.948974920988083\n",
      "第328000轮迭代后的损失为：4.001699679493904\n",
      "第330000轮迭代后的损失为：4.026540429472924\n",
      "与单词 如 最相似的：  似,  宙,  于,  非,  拖,  愚,  夭,  冱, \n",
      "与单词 金 最相似的：  宝,  雕,  玉,  茁,  瓷,  罍,  逗,  芘, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  贰,  易, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  ＊,  孰,  壮,  缦,  少, \n",
      "与单词 小 最相似的：  儿,  掏,  侧,  簇,  危,  拥,  斜,  打, \n",
      "与单词 今 最相似的：  昨,  此,  终,  它,  备,  永,  吊,  何, \n",
      "与单词 仙 最相似的：  蓠,  棺,  帖,  朅,  炁,  真,  姜,  驳, \n",
      "与单词 是 最相似的：  在,  有,  把,  惬,  做,  过,  向,  误, \n",
      "与单词 此 最相似的：  今,  屡,  快,  後,  法,  且,  偈,  我, \n",
      "与单词 似 最相似的：  如,  在,  胜,  记,  做,  是,  奈,  知, \n",
      "与单词 里 最相似的：  中,  入,  闭,  与,  救,  蹁,  洲,  坼, \n",
      "与单词 心 最相似的：  情,  愁,  鶒,  筒,  矧,  养,  昽,  匡, \n",
      "与单词 行 最相似的：  辇,  散,  营,  甑,  谊,  侑,  茶,  绻, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  闭,  宠,  剩,  敝, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  努,  羡,  许,  枳, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  菩,  緺,  砢,  鹧, \n",
      "第332000轮迭代后的损失为：4.004554540872574\n",
      "第334000轮迭代后的损失为：4.019993456840515\n",
      "第336000轮迭代后的损失为：3.9336764492988587\n",
      "第338000轮迭代后的损失为：3.9409296301603316\n",
      "第340000轮迭代后的损失为：3.950382835328579\n",
      "与单词 如 最相似的：  似,  宙,  拖,  于,  冱,  曝,  夭,  在, \n",
      "与单词 金 最相似的：  宝,  雕,  芘,  玉,  瓷,  茁,  频,  罍, \n",
      "与单词 未 最相似的：  不,  乍,  先,  难,  已,  欲,  贰,  无, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  日,  孰,  壮,  记,  ＊, \n",
      "与单词 小 最相似的：  掏,  儿,  侧,  斜,  危,  簇,  谯,  午, \n",
      "与单词 今 最相似的：  昨,  终,  此,  它,  备,  永,  吊,  何, \n",
      "与单词 仙 最相似的：  蓠,  朅,  棺,  帖,  炁,  姜,  真,  跻, \n",
      "与单词 是 最相似的：  有,  在,  把,  向,  似,  做,  惬,  被, \n",
      "与单词 此 最相似的：  今,  屡,  快,  後,  法,  偈,  且,  毕, \n",
      "与单词 似 最相似的：  如,  在,  是,  胜,  做,  待,  见,  知, \n",
      "与单词 里 最相似的：  中,  与,  入,  闭,  救,  蹁,  洲,  壑, \n",
      "与单词 心 最相似的：  情,  矧,  匡,  筒,  愁,  鶒,  意,  养, \n",
      "与单词 行 最相似的：  辇,  营,  侑,  谊,  甑,  散,  茶,  拏, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  敝,  闭,  宠,  同, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  许,  努,  羡,  枳, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  緺,  ）,  菩,  砢,  劬, \n",
      "第342000轮迭代后的损失为：3.978455351829529\n",
      "第344000轮迭代后的损失为：4.023073364019394\n",
      "第346000轮迭代后的损失为：4.011595504522323\n",
      "第348000轮迭代后的损失为：4.0072078008651735\n",
      "第350000轮迭代后的损失为：3.9492085040807723\n",
      "与单词 如 最相似的：  似,  宙,  冱,  于,  拖,  为,  在,  夭, \n",
      "与单词 金 最相似的：  宝,  玉,  雕,  罍,  茁,  芘,  瓷,  脯, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  先,  无,  欲,  贰, \n",
      "与单词 年 最相似的：  岁,  壮,  琲,  日,  时,  ＊,  记,  饶, \n",
      "与单词 小 最相似的：  儿,  掏,  簇,  危,  侧,  画,  拥,  谯, \n",
      "与单词 今 最相似的：  昨,  此,  终,  它,  永,  备,  何,  吊, \n",
      "与单词 仙 最相似的：  蓠,  朅,  驳,  棺,  帖,  真,  炁,  跻, \n",
      "与单词 是 最相似的：  在,  有,  把,  作,  向,  似,  做,  到, \n",
      "与单词 此 最相似的：  今,  快,  屡,  後,  法,  我,  且,  偈, \n",
      "与单词 似 最相似的：  如,  在,  是,  知,  记,  胜,  辈,  见, \n",
      "与单词 里 最相似的：  中,  闭,  疣,  入,  户,  救,  蹁,  坼, \n",
      "与单词 心 最相似的：  情,  矧,  愁,  鶒,  筒,  昽,  意,  害, \n",
      "与单词 行 最相似的：  营,  辇,  谊,  侑,  程,  恕,  甑,  拏, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  闭,  闺,  宠,  嫦, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  努,  羡,  似,  许, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  緺,  淘,  ）,  鹧, \n",
      "第352000轮迭代后的损失为：3.9404660968780516\n",
      "第354000轮迭代后的损失为：3.94156556558609\n",
      "第356000轮迭代后的损失为：3.960546412110329\n",
      "第358000轮迭代后的损失为：4.013005190253258\n",
      "第360000轮迭代后的损失为：4.026954198002815\n",
      "与单词 如 最相似的：  似,  宙,  拖,  于,  冱,  为,  曝,  等, \n",
      "与单词 金 最相似的：  宝,  玉,  雕,  茁,  瓷,  罍,  股,  逗, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  无,  贰, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  日,  壮,  咀,  ＊,  记, \n",
      "与单词 小 最相似的：  儿,  掏,  簇,  危,  画,  侧,  谯,  喔, \n",
      "与单词 今 最相似的：  昨,  此,  终,  它,  备,  何,  昔,  疆, \n",
      "与单词 仙 最相似的：  蓠,  棺,  朅,  真,  帖,  姜,  炁,  驳, \n",
      "与单词 是 最相似的：  在,  有,  把,  似,  向,  做,  作,  惬, \n",
      "与单词 此 最相似的：  今,  快,  屡,  後,  法,  我,  且,  偈, \n",
      "与单词 似 最相似的：  如,  是,  胜,  在,  知,  做,  记,  与, \n",
      "与单词 里 最相似的：  中,  与,  壑,  入,  救,  闭,  洲,  沉, \n",
      "与单词 心 最相似的：  情,  鶒,  矧,  筒,  昽,  养,  愁,  害, \n",
      "与单词 行 最相似的：  营,  辇,  谊,  程,  茶,  恕,  甑,  瘁, \n",
      "与单词 重 最相似的：  还,  再,  叠,  舵,  又,  敝,  嫦,  剩, \n",
      "与单词 知 最相似的：  信,  识,  酎,  敻,  许,  努,  羡,  认, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  菩,  緺,  花,  淘, \n",
      "第362000轮迭代后的损失为：3.9998023471832274\n",
      "第364000轮迭代后的损失为：3.9557387499809264\n",
      "第366000轮迭代后的损失为：3.9550050488710404\n",
      "第368000轮迭代后的损失为：3.9232728765010836\n",
      "第370000轮迭代后的损失为：3.955014137506485\n",
      "与单词 如 最相似的：  似,  宙,  拖,  冱,  于,  为,  等,  躲, \n",
      "与单词 金 最相似的：  宝,  玉,  茁,  雕,  罍,  溜,  瓷,  频, \n",
      "与单词 未 最相似的：  不,  乍,  难,  已,  先,  欲,  无,  贰, \n",
      "与单词 年 最相似的：  岁,  琲,  时,  □,  壮,  孰,  日,  记, \n",
      "与单词 小 最相似的：  掏,  侧,  儿,  危,  簇,  排,  打,  喔, \n",
      "与单词 今 最相似的：  昨,  它,  终,  此,  备,  吊,  疆,  来, \n",
      "与单词 仙 最相似的：  蓠,  棺,  朅,  帖,  驳,  炁,  姜,  缥, \n",
      "与单词 是 最相似的：  有,  在,  把,  似,  向,  □,  做,  过, \n",
      "与单词 此 最相似的：  快,  今,  屡,  且,  後,  法,  偈,  我, \n",
      "与单词 似 最相似的：  如,  是,  胜,  在,  知,  记,  问,  做, \n",
      "与单词 里 最相似的：  闭,  入,  壑,  中,  洲,  与,  救,  户, \n",
      "与单词 心 最相似的：  情,  矧,  愁,  匡,  筒,  意,  养,  鶒, \n",
      "与单词 行 最相似的：  营,  辇,  谊,  程,  侑,  茶,  恕,  拏, \n",
      "与单词 重 最相似的：  还,  再,  叠,  闺,  □,  敝,  闭,  茎, \n",
      "与单词 知 最相似的：  信,  识,  敻,  酎,  许,  努,  认,  羡, \n",
      "与单词 。 最相似的：  ，,  、,  ）,  \n",
      ",  筲,  菩,  緺,  聒, \n",
      "第372000轮迭代后的损失为：3.99814896440506\n",
      "第374000轮迭代后的损失为：4.011320822954178\n",
      "第376000轮迭代后的损失为：3.9870246523618698\n",
      "第378000轮迭代后的损失为：3.985685503602028\n",
      "第380000轮迭代后的损失为：3.9293342752456666\n",
      "与单词 如 最相似的：  似,  宙,  于,  为,  拖,  冱,  在,  夭, \n",
      "与单词 金 最相似的：  宝,  玉,  频,  罍,  芘,  雕,  鳊,  瓷, \n",
      "与单词 未 最相似的：  不,  乍,  难,  先,  已,  欲,  无,  贰, \n",
      "与单词 年 最相似的：  岁,  时,  壮,  琲,  日,  缦,  忭,  孰, \n",
      "与单词 小 最相似的：  掏,  侧,  儿,  画,  危,  斜,  打,  靓, \n",
      "与单词 今 最相似的：  昨,  此,  它,  终,  永,  备,  来,  何, \n",
      "与单词 仙 最相似的：  蓠,  朅,  棺,  驳,  螟,  帖,  姜,  阆, \n",
      "与单词 是 最相似的：  在,  有,  把,  作,  向,  似,  做,  惬, \n",
      "与单词 此 最相似的：  今,  快,  後,  也,  屡,  法,  我,  偈, \n",
      "与单词 似 最相似的：  如,  在,  是,  胜,  记,  知,  亦,  奈, \n",
      "与单词 里 最相似的：  闭,  中,  与,  入,  户,  疣,  蹁,  坼, \n",
      "与单词 心 最相似的：  情,  愁,  意,  矧,  筒,  匡,  鶒,  昽, \n",
      "与单词 行 最相似的：  营,  辇,  程,  谊,  茶,  侑,  恕,  庶, \n",
      "与单词 重 最相似的：  再,  还,  叠,  舵,  同,  敝,  闺,  茎, \n",
      "与单词 知 最相似的：  信,  识,  敻,  酎,  许,  羡,  努,  认, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  菩,  ）,  砢,  抨,  鹧, \n",
      "第382000轮迭代后的损失为：3.9282142778635025\n",
      "第384000轮迭代后的损失为：3.9262661928534506\n",
      "第386000轮迭代后的损失为：3.98542529630661\n",
      "第388000轮迭代后的损失为：4.017641028046608\n",
      "第390000轮迭代后的损失为：3.9961392587423323\n",
      "与单词 如 最相似的：  似,  宙,  于,  冱,  谆,  为,  等,  拖, \n",
      "与单词 金 最相似的：  宝,  雕,  玉,  芘,  茁,  瓷,  罍,  频, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  贰,  初, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  壮,  记,  日,  孰,  缦, \n",
      "与单词 小 最相似的：  掏,  儿,  侧,  危,  斜,  打,  靓,  喔, \n",
      "与单词 今 最相似的：  昨,  此,  它,  终,  备,  何,  吊,  永, \n",
      "与单词 仙 最相似的：  蓠,  朅,  棺,  炁,  驳,  帖,  跻,  真, \n",
      "与单词 是 最相似的：  在,  有,  把,  做,  作,  惬,  似,  也, \n",
      "与单词 此 最相似的：  今,  快,  後,  法,  屡,  偈,  也,  且, \n",
      "与单词 似 最相似的：  如,  记,  是,  在,  胜,  做,  与,  见, \n",
      "与单词 里 最相似的：  中,  闭,  与,  入,  后,  洲,  户,  沉, \n",
      "与单词 心 最相似的：  矧,  情,  筒,  愁,  鶒,  昽,  害,  意, \n",
      "与单词 行 最相似的：  营,  辇,  茶,  谊,  侑,  恕,  散,  庶, \n",
      "与单词 重 最相似的：  再,  叠,  还,  敝,  舵,  又,  闺,  茎, \n",
      "与单词 知 最相似的：  信,  识,  许,  敻,  酎,  认,  胆,  努, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  菩,  緺,  劬,  砢, \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第392000轮迭代后的损失为：3.997267390370369\n",
      "第394000轮迭代后的损失为：3.921929252028465\n",
      "第396000轮迭代后的损失为：3.927761765599251\n",
      "第398000轮迭代后的损失为：3.9419717592597006\n",
      "第400000轮迭代后的损失为：3.946257163286209\n",
      "与单词 如 最相似的：  似,  宙,  冱,  为,  等,  于,  嚎,  必, \n",
      "与单词 金 最相似的：  宝,  雕,  茁,  玉,  芘,  频,  瓷,  牡, \n",
      "与单词 未 最相似的：  不,  乍,  已,  难,  先,  欲,  贰,  无, \n",
      "与单词 年 最相似的：  岁,  时,  琲,  日,  老,  壮,  ＊,  孰, \n",
      "与单词 小 最相似的：  掏,  侧,  危,  儿,  打,  排,  靓,  斜, \n",
      "与单词 今 最相似的：  昨,  它,  终,  此,  来,  备,  永,  前, \n",
      "与单词 仙 最相似的：  蓠,  棺,  朅,  姜,  鍪,  阆,  驳,  掞, \n",
      "与单词 是 最相似的：  在,  有,  把,  似,  做,  向,  作,  过, \n",
      "与单词 此 最相似的：  快,  今,  法,  偈,  後,  屡,  且,  惟, \n",
      "与单词 似 最相似的：  如,  在,  是,  记,  胜,  与,  做,  向, \n",
      "与单词 里 最相似的：  闭,  入,  中,  与,  洲,  户,  壑,  救, \n",
      "与单词 心 最相似的：  情,  矧,  愁,  匡,  昽,  害,  鶒,  筒, \n",
      "与单词 行 最相似的：  营,  辇,  茶,  侑,  缓,  散,  谊,  甑, \n",
      "与单词 重 最相似的：  再,  还,  叠,  同,  又,  闺,  茎,  舵, \n",
      "与单词 知 最相似的：  信,  识,  敻,  许,  认,  酎,  努,  枳, \n",
      "与单词 。 最相似的：  ，,  、,  \n",
      ",  ）,  緺,  菩,  ；,  鸪, \n"
     ]
    }
   ],
   "source": [
    "##9.启动训练\n",
    "num_steps = 400001 #进行15W次的迭代计算\n",
    "#创建一个回话并设置为默认\n",
    "with tf.Session(graph=graph) as session:\n",
    "    init.run() #启动参数的初始化\n",
    "    print(\"初始化完成！\")\n",
    "    average_loss = 0 #计算误差\n",
    "\n",
    "    #开始迭代训练\n",
    "    for step in range(num_steps):\n",
    "        batch_inputs,batch_labels = generate_batch(batch_size,num_skips,skip_window) #调用生成训练数据函数生成一组batch和label\n",
    "        feed_dict = {train_inputs:batch_inputs,train_labels:batch_labels} #待填充的数据\n",
    "        #启动回话，运行优化器optimizer和损失计算函数，并填充数据\n",
    "        optimizer_trained,loss_val = session.run([optimizer,loss],feed_dict=feed_dict)\n",
    "        average_loss += loss_val #统计NCE损失\n",
    "\n",
    "        #为了方便，每2000次计算一下损失并显示出来\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "            print(\"第{}轮迭代后的损失为：{}\".format(step,average_loss))\n",
    "            average_loss = 0\n",
    "\n",
    "        #每10000次迭代，计算一次验证单词与全部单词的相似度，并将于验证单词最相似的前8个单词呈现出来\n",
    "        if step % 10000 == 0:\n",
    "            sim = similarity.eval() #计算向量\n",
    "            for i in range(valid_size):\n",
    "                valid_word = reverse_dictionary[valid_examples[i]] #得到对应的验证单词\n",
    "                top_k = 8\n",
    "                nearest = (-sim[i,:]).argsort()[1:top_k+1] #计算每一个验证单词相似度最接近的前8个单词\n",
    "                log_str = \"与单词 {} 最相似的： \".format(str(valid_word))\n",
    "\n",
    "                for k in range(top_k):\n",
    "                    close_word = reverse_dictionary[nearest[k]] #相似度高的单词\n",
    "                    log_str = \"%s %s, \" %(log_str,close_word)\n",
    "                print(log_str)\n",
    "    final_embeddings = normalized_embeddings.eval()\n",
    "    np.save('embedding.npy', final_embeddings)\n",
    "    with open(FLAGS.log_dir + '/metadata.tsv', 'w', encoding=\"UTF-8\") as f:\n",
    "        for i in xrange(vocabulary_size):\n",
    "            f.write(reverse_dictionary[i] + '\\n')\n",
    "    saver.save(session, os.path.join(FLAGS.log_dir, 'model.ckpt'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/limeng/anaconda3/lib/python3.6/site-packages/matplotlib/mpl-data/matplotlibrc\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.matplotlib_fname())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#下载SimHei.ttf，修改配置\n",
    "from matplotlib.font_manager import _rebuild\n",
    "_rebuild() #reload一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WTS+//LL27t2rU6dO6fnnn1d4eLhsNhubSQMot67paWIWiyVMUhPDMNpYLJa/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P/99+avbdiwQd4HiEJJpUS/A09PT1NoaGiBP64QQgiRV02bNkWj0QBw9epVrKysqFy5MpA11nr3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VOGFStWMG3aNORyOUaj0SI2kWjcuLEo/giCxEQxSBAKmUwmY9u2bU/NDHr8+DGTJk2SOF3J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\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u7s64ceN05S6CIORPq9Xi4eHB0aNHAfj1119xc3PDzc0NhUIUYQiCvmi1WoYPH04N9xqs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\nyXPqlFxz5qokK0tjx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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.可视化Word2Vec效果\n",
    "def plot_with_labels(low_dim_embs,labels,filename = \"tsne.png\"):\n",
    "    assert low_dim_embs.shape[0] >= len(labels),\"标签数超过了嵌入向量的个数！！\"\n",
    "\n",
    "    plt.figure(figsize=(20,20))\n",
    "    for i,label in enumerate(labels):\n",
    "        x,y = low_dim_embs[i,:]\n",
    "        plt.scatter(x,y)\n",
    "        plt.annotate(\n",
    "            label,\n",
    "            xy = (x,y),\n",
    "            xytext=(5,2),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"right\",\n",
    "            va=\"bottom\"\n",
    "        )\n",
    "    plt.savefig(filename)\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family']='sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']\n",
    "\n",
    "tsne = TSNE(perplexity=30,n_components=2,init=\"pca\",n_iter=5000, method='exact')\n",
    "plot_only = 500\n",
    "low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\n",
    "Labels = [reverse_dictionary[i] for i in range(plot_only)]\n",
    "plot_with_labels(low_dim_embs,Labels)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
